Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm
Earthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes an...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Advances in Civil Engineering |
| Online Access: | http://dx.doi.org/10.1155/2020/6548682 |
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| author | Hanxi Jia Junqi Lin Jinlong Liu |
| author_facet | Hanxi Jia Junqi Lin Jinlong Liu |
| author_sort | Hanxi Jia |
| collection | DOAJ |
| description | Earthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes and save rescue time. This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. The input variables of the ANN model were the factors with higher importance value, and the output variables were damage states. The data of the Wenchuan earthquake were used as a testing set and a training set, and the data of the Tangshan earthquake were used as a validation set. The bridges under serious and complete damage states are not accessible after earthquakes and should be overhauled and reinforced before earthquakes. The results demonstrate that the proposed approach has good performance for assessing the damage states of the two bridges. It is robust enough to extend and improve emergency decisions, to save time for rescue work, and to help with bridge construction. |
| format | Article |
| id | doaj-art-7d675c92895b4ea3b77ba285b2770771 |
| institution | Kabale University |
| issn | 1687-8086 1687-8094 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Advances in Civil Engineering |
| spelling | doaj-art-7d675c92895b4ea3b77ba285b27707712025-02-03T01:04:20ZengWileyAdvances in Civil Engineering1687-80861687-80942020-01-01202010.1155/2020/65486826548682Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest AlgorithmHanxi Jia0Junqi Lin1Jinlong Liu2Institute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaInstitute of Engineering Mechanics, China Earthquake Administration, Harbin 150080, ChinaEarthquakes cause significant damage to bridges, which have a very strategic location in transportation services. The destruction of a bridge will seriously hinder emergency rescue. Rapid assessment of bridge seismic damage can help relevant departments to make judgments quickly after earthquakes and save rescue time. This paper proposed a rapid assessment method for bridge seismic damage based on the random forest algorithm (RF) and artificial neural networks (ANN). This method evaluated the relative importance of each uncertain influencing factor of the seismic damage to the girder bridges and arch bridges, respectively. The input variables of the ANN model were the factors with higher importance value, and the output variables were damage states. The data of the Wenchuan earthquake were used as a testing set and a training set, and the data of the Tangshan earthquake were used as a validation set. The bridges under serious and complete damage states are not accessible after earthquakes and should be overhauled and reinforced before earthquakes. The results demonstrate that the proposed approach has good performance for assessing the damage states of the two bridges. It is robust enough to extend and improve emergency decisions, to save time for rescue work, and to help with bridge construction.http://dx.doi.org/10.1155/2020/6548682 |
| spellingShingle | Hanxi Jia Junqi Lin Jinlong Liu Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm Advances in Civil Engineering |
| title | Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm |
| title_full | Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm |
| title_fullStr | Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm |
| title_full_unstemmed | Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm |
| title_short | Bridge Seismic Damage Assessment Model Applying Artificial Neural Networks and the Random Forest Algorithm |
| title_sort | bridge seismic damage assessment model applying artificial neural networks and the random forest algorithm |
| url | http://dx.doi.org/10.1155/2020/6548682 |
| work_keys_str_mv | AT hanxijia bridgeseismicdamageassessmentmodelapplyingartificialneuralnetworksandtherandomforestalgorithm AT junqilin bridgeseismicdamageassessmentmodelapplyingartificialneuralnetworksandtherandomforestalgorithm AT jinlongliu bridgeseismicdamageassessmentmodelapplyingartificialneuralnetworksandtherandomforestalgorithm |